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IJHCS
Published by Research Parks Publishing
ISSN : 26151898     EISSN : 26158159     DOI : https://doi.org/10.31149/ijhcs.v7i1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Neuroscience
The International Journal of Human Computing Studies (IJHCS) publishes original research over the whole spectrum of work relevant to the theory and practice of modern interactive systems of the contemporary world. IJHCS accepts papers in forms of original research articles, review articles, book reviews, case reports, and discussions to answer important and interesting questions of the innovative research points.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 3 Documents
 1 
Search results for , issue "Vol. 3 No. 6 (2021): IJHCS" : 3 Documents clear
The Foundation of the Third Renaissance is a Spiritual Ascent Sayyora, Botirova
International Journal of Human Computing Studies Vol. 3 No. 6 (2021): IJHCS
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v3i6.2080

Abstract

spirituality is such a factor that determines the development of society, the perfection of people and nation and the perfection of personality, is the value that gives Man the essence and essence of humanity, elevates a person from Earth to heaven, encourages him to goodness. The absence of a branch of Hech, which is not directly related to the spirituality of science, is a fact that does not require proof.
Hybridization of Machine Learning Techniques in Predicting Mental Disorder Alabi, Emmanuel Oluwadunsin; Adeniji, Oluwashola David; Awoyelu, Tolulope Moyo; Fasae, Oluwakemi Dunsin
International Journal of Human Computing Studies Vol. 3 No. 6 (2021): IJHCS
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v3i6.2083

Abstract

This study applied a hybrid Random Forest and Artificial Neural Network (RF-ANN) model in predicting the chances of IT employees developing mental disorder. To measure the performance of the model, Random Forest and Artificial Neural Networks algorithms were separately developed, their results were recorded and compared with the results of the hybridized model. The result obtained from the hybridized model showed a significant improvement in its performance over the individual performances of the Random Forest model and Artificial Neural Networks models. Hybridizing Random Forest and Artificial Neural Networks using “Bagging Ensemble” produced a model that was able to correctly predict the chances of IT employees developing Mental Disorder with 94% recall and 80% F1-score compared to 65% and 60% respectively in the Random Forest Model. With these results, applying the RF-ANN model on improved dataset could be investigated and compared with the results found in this study.
Music Genre Classification Using 1D Convolution Neural Network Falola, Peace Busola; Akinola, Solomon Olalekan
International Journal of Human Computing Studies Vol. 3 No. 6 (2021): IJHCS
Publisher : Research Parks Publishing LLC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31149/ijhcs.v3i6.2108

Abstract

Music genre classification system is a system that is important to the users for effectiveness in the digital music industry. One of the effective ways of genre classification is in music recommendation and access to users. With accurate classification system built, songs can be readily accessed by the users when the genre of the song is known and recommendation of songs to the users is made easy. Also, automatic classification of genre is important to solve problems such as tracking down related songs, discovering societies that will like specific songs and also for survey purposes. In recent times, deep learning techniques have proven to be effective in several classification tasks including music genre classification. This paper therefore examines the application of 1D Convolutional Neural Network for music genre classification. A new dataset consisting of 1000 Nigerian traditional songs with seven genres was used for this work. As features extraction is crucial to audio analysis, seven low level features also known as content based features were extracted from the songs in the dataset which served as input into the classifier. Our results showed that the accuracy level of the system is 92.5% with a precision of 92.7%, recall of 92.5% and f1 score of 92.5%.

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